JIT hooks

There are several hooks in the pypyjit module that may help you with understanding what’s pypy’s JIT doing while running your program. There are three functions related to that coming from the pypyjit module:

set_optimize_hook(callable)

Set a compiling hook that will be called each time a loop is optimized, but before assembler compilation. This allows adding additional optimizations on Python level.

The callable will be called with the pypyjit.JitLoopInfo object. Refer to it’s documentation for details.

Result value will be the resulting list of operations, or None

set_compile_hook(callable)

Set a compiling hook that will be called each time a loop is compiled.

The callable will be called with the pypyjit.JitLoopInfo object. Refer to it’s documentation for details.

Note that jit hook is not reentrant. It means that if the code inside the jit hook is itself jitted, it will get compiled, but the jit hook won’t be called for that.

set_abort_hook(hook)

Set a hook (callable) that will be called each time there is tracing aborted due to some reason.

The hook will be invoked with the siagnture: hook(jitdriver_name, greenkey, reason, oplist)

Reason is a string, the meaning of other arguments is the same as attributes on JitLoopInfo object

enable_debug()

Start recording debugging counters for get_stats_snapshot

disable_debug()

Stop recording debugging counters for get_stats_snapshot

get_stats_snapshot()

Get the jit status in the specific moment in time. Note that this is eager - the attribute access is not lazy, if you need new stats you need to call this function again. You might want to call enable_debug to get more information. It returns an instance of JitInfoSnapshot

class JitInfoSnapshot

A class describing current snapshot. Usable attributes:

• counters - internal JIT integer counters
• counter_times - internal JIT float counters, notably time spent TRACING and in the JIT BACKEND
• loop_run_times - counters for number of times loops are run, only works when enable_debug is called.